yongmei ye

Dr. yongmei ye


Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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11 December 2022 in Agronomy
Verified
Journal Article
Open access
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YYyongmei ye
yongmei ye
PZPing Zou
Ping Zou
WZWeihang Zhang
Weihang Zhang
XLXie Qihua Liu
Xie Qihua Liu
BLBin Liu
Bin Liu
XKXiaolan Kang
Xiaolan Kang
To promote the resilient and coordinated development of regional economies, in this study, to construct an index system, we used the entropy weight method to measure the agricultural economic resilience of 11 prefecture-level cities in Jiangxi province, China during 2011–2020, and we analyzed the characteristics of their spatial-temporal pattern evolution. We used the Theil index method to analyze the main sources of their spatial differences. The results showed that: First, the mean value of agricultural economic resilience of prefecture-level cities in Jiangxi trended upward during 2011–2020. Among the prefecture-level cities, Ganzhou had the highest mean agricultural economic resilience value, while Yingtan had the lowest. From the perspective of regional division, the agricultural economy in southern Jiangxi was the most resilient, whereas that in northeast Jiangxi was the least. Second, since 2011, the overall difference in agricultural economic resilience in the four regions of Jiangxi has trended downward, with the inter-regional difference being the main source of the overall difference. Based on this, we provide the following policy implications: first, to continuously enhance the intrinsic dynamics of the agricultural economic resilience and strengthen policy support in Jiangxi; second, to coordinate the development of regional agricultural economic resilience and achieve overall improvement.

23 September 2023 in Sustainability
Verified
Journal Article
Open access
PDF file
SYsong yu
song yu
LYlulu Yang
lulu Yang
ZSZhenjiang Song
Zhenjiang Song
WLWenbo Li
Wenbo Li
YYyongmei ye
yongmei ye
BLBin Liu
Bin Liu
To accurately identify the land ecological security in the middle and lower reaches of the Yangtze River, we measured the land ecological security based on the PSR model, selected a total of 19 indicators, such as population density, and applied the methods of system evaluation value conversion, comprehensive evaluation value and coordination degree measurement to study the land ecological security in the region. Research findings: The comprehensive evaluation level showed that Jiangxi, Shanghai, and Hunan had comprehensive evaluation values according to the regional forefront. The subsystem evaluation level showed that Shanghai and Hunan had higher pressure subsystem evaluation values relative to other provinces; Shanghai, Zhejiang, and Anhui had higher state subsystem evaluation values relative to other provinces; and Jiangxi was much more responsive to land ecological security issues than were other provinces and cities in the region. The system coordination degree showed that the comprehensive evaluation value of land ecological safety and the coordination degree evaluation value of other provinces except Shanghai and Hunan showed a negative correlation, indicating certain land ecological safety problems in the region. Finally, suggested reflections based on the conclusions are presented.

1 November 2022 in Sustainability
Verified
Journal Article
Open access
PDF file
XLXie Qihua Liu
Xie Qihua Liu
YYyongmei ye
yongmei ye
DGDongdong Ge
Dongdong Ge
ZWZhen Wang
Zhen Wang
BLBin Liu
Bin Liu
The study of the evolutionary patterns and trends of agricultural carbon emission intensity and agricultural economic development levels plays an important role in promoting the green and low-carbon sustainable development of agriculture. This paper adopts the carbon emission factor method to measure the agricultural carbon emissions in Jiangxi Province from 2001 to 2020, uses the LMDI decomposition method to explore the drivers of carbon emissions, and further analyzes the coupling relationship between agricultural carbon emissions and the agricultural economy using the Tapio decoupling model, based on which a GM (1,1) model is used to forecast the agricultural carbon emissions in Jiangxi Province from 2001 to 2015. According to the research results, agricultural carbon emissions in Jiangxi Province show a trend of “rising and then falling”, with the intensity decreasing; the level of economic development is the main factor that increases carbon emissions, while the efficiency of agricultural production, the size of the labor force, and the structure of agricultural production have positive effects in terms of reducing carbon emissions. How to reduce carbon emissions while promoting agricultural economic development is an issue that remains to be addressed in the future. Further analysis found that the decoupling states of Jiangxi Province from 2001 to 2009 switched between strong decoupling and weak decoupling, with weak decoupling dominating the years 2010–2015 and strong decoupling dominating from 2016 onwards. With the continuous promotion of carbon emission reduction, agricultural carbon emissions in Jiangxi Province will continue to show a decreasing trend over the next five years. Three policy recommendations are put forth in order to advance the effort to reduce agricultural carbon emissions in Jiangxi Province: cultivating high-quality and low-carbon rice varieties, switching to green agricultural production, and coordinating the connection between economic growth and agricultural carbon emissions.